Collaborative Mapping with IoE-based Heterogeneous Vehicles for Enhanced Situational Awareness

Jorge Peña Queralta, Tuan Nguyen Gia, Hannu Tenhunen and Tomi Westerlund

The development of autonomous vehicles or advanced driving assistance platforms has had a great leap forward getting closer to human daily life over the last decade. Nevertheless, it is still challenging to achieve an efficient and fully autonomous vehicle or driving assistance platform due to many strict requirements and complex situations or unknown environments. One of the main remaining challenges is a robust situational awareness in autonomous vehicles in unknown environments. An autonomous system with a poor situation awareness due to low quantity or quality of data may directly or indirectly cause serious consequences. For instance, a person's life might be at risk due to a delay caused by a long or incorrect path planning of an autonomous ambulance. Internet of Everything (IoE) is currently becoming a prominent technology for many applications such as automation. In this paper, we propose an IoE-based architecture consisting of a heterogeneous team of cars and drones for enhancing situational awareness in autonomous cars, especially when dealing with critical cases of natural disasters. In particular, we show how an autonomous car can plan in advance the possible paths to a given destination, and send orders to other vehicles. These, in turn, perform terrain reconnaissance for avoiding obstacles and dealing with difficult situations. Together with a map merging algorithm deployed into the team, the proposed architecture can help to save traveling distance and time significantly in case of complex scenarios.

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